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Development of sediment load estimation models by using artificial neural networking techniques

Environmental Monitoring and Assessment volume 187, Article number: 686 (2015) Cite this article

Abstract

This study aims at the development of an artificial neural network-based model for the estimation of weekly sediment load at a catchment located in northern part of Pakistan. The adopted methodology has been based upon antecedent sediment conditions, discharge, and temperature information. Model input and data length selection was carried out using a novel mathematical tool, Gamma test. Model training was carried out by using three popular algorithms namely Broyden-Fletcher-Goldfarb-Shanno (BFGS), back propagation (BP), and local linear regression (LLR) using forward selection of input variables. Evaluation of the best model was carried out on the basis of basic statistical parameters namely R-square, root mean squared error (RMSE), and mean biased error (MBE). Results indicated that BFGS-based ANN model outperformed all other models with significantly low values of RMSE and MBE. A strong correlation was also found between the observed and estimated sediment load values for the same model as the value of Nash-Sutcliffe model efficiency coefficient (R-square) was found to be quite high as well.

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Affiliations

  1. Department of Civil Engineering, Mirpur University of Science & Technology, Mirpur, A.K., Pakistan

    Muhammad Hassan, Imran Mehmood, Imtiaz Ahmed, Syed Zishan Ashiq & Anwar Khitab

  2. Department of Civil Engineering, Bursa Orhangazi Üniversitesi, Bursa, Turkey

    M. Ali Shamim

  3. Department of Civil Engineering, National University of Sciences & Technology, Islamabad, Pakistan

    Ali Sikandar

Authors
  1. Muhammad Hassan
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  2. M. Ali Shamim
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  3. Ali Sikandar
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  4. Imran Mehmood
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  5. Imtiaz Ahmed
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  6. Syed Zishan Ashiq
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  7. Anwar Khitab
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Correspondence to Muhammad Hassan.

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Hassan, M., Ali Shamim, M., Sikandar, A. et al. Development of sediment load estimation models by using artificial neural networking techniques. Environ Monit Assess 187, 686 (2015). https://doi.org/10.1007/s10661-015-4866-y

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  • DOI: https://doi.org/10.1007/s10661-015-4866-y

Keywords

  • Sedimentation
  • Dead storage
  • Physical parameters
  • Gamma test
  • Artificial neural networks